Checklist for Improving the Superset Integration for a Database
Blog post from Preset
Apache Superset initially supported only Apache Druid using its native API, but as Druid evolved to support SQL, the need to extend Superset's compatibility with various databases grew. Today, Superset accommodates a wide array of SQL-speaking databases, enhancing its querying capabilities through SQLAlchemy dialects and Python DB-API libraries. The article details the necessary attributes and methods to develop custom database engines, such as defining time grain expressions for time-series visualizations, converting Python datetime objects to SQL expressions, and adapting SQLAlchemy connection strings. Additionally, it discusses recommended practices like mapping database-specific exceptions to Superset's exceptions, defining column type mappings, and handling epoch conversions to datetime formats. The document provides examples from databases like CrateDB and Postgres, illustrating how Superset can be customized for enhanced user experiences through features like query feedback, column name handling, and SQL Lab auto-completion. Overall, the guide offers an in-depth exploration of developing and optimizing database engines for seamless integration with Superset.
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